A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Text mining and natural language processing in construction
Abstract Text mining (TM) and natural language processing (NLP) have stirred interest within the construction field, as they offer enhanced capabilities for managing and analyzing text-based information. This highlights the need for a systematic review to identify the status quo, gaps, and future directions from the perspective of construction management. A review was conducted by aligning the objectives of 205 publications with the specific domains, areas, tasks, and processes outlined in construction management practices. This review reveals multiple facets of the construction sector empowered by TM/NLP approaches and highlights essential voids demanding consideration for automation possibilities and minimizing manual tasks. Ultimately, following identified obstacles, the review results indicate potential research opportunities: (1) strengthening overlooked construction aspects, (2) coupling diverse data formats, and (3) leveraging pre-trained language models and reinforcement learning. The findings will provide vital insights, fostering further progress in TM/NLP research and its applications in academia and industry.
Graphical abstract Display Omitted
Highlights A review on 205 articles pertaining to the applications of text mining and natural language processing in the construction. Ongoing challenges, potential solutions, and emerging research themes have been investigated. Recommendations have been presented to incorporate a combination of different data types to enhance knowledge areas. Suggestions have been given to utilize pre-trained language models, large language models, and reinforcement learning.
Text mining and natural language processing in construction
Abstract Text mining (TM) and natural language processing (NLP) have stirred interest within the construction field, as they offer enhanced capabilities for managing and analyzing text-based information. This highlights the need for a systematic review to identify the status quo, gaps, and future directions from the perspective of construction management. A review was conducted by aligning the objectives of 205 publications with the specific domains, areas, tasks, and processes outlined in construction management practices. This review reveals multiple facets of the construction sector empowered by TM/NLP approaches and highlights essential voids demanding consideration for automation possibilities and minimizing manual tasks. Ultimately, following identified obstacles, the review results indicate potential research opportunities: (1) strengthening overlooked construction aspects, (2) coupling diverse data formats, and (3) leveraging pre-trained language models and reinforcement learning. The findings will provide vital insights, fostering further progress in TM/NLP research and its applications in academia and industry.
Graphical abstract Display Omitted
Highlights A review on 205 articles pertaining to the applications of text mining and natural language processing in the construction. Ongoing challenges, potential solutions, and emerging research themes have been investigated. Recommendations have been presented to incorporate a combination of different data types to enhance knowledge areas. Suggestions have been given to utilize pre-trained language models, large language models, and reinforcement learning.
Text mining and natural language processing in construction
Shamshiri, Alireza (author) / Ryu, Kyeong Rok (author) / Park, June Young (author)
2023-11-13
Article (Journal)
Electronic Resource
English
Construction site accident analysis using text mining and natural language processing techniques
| British Library Online Contents | 2019
| Taylor & Francis Verlag | 2024